Hybrid Ant Colony Robust Genetic Algorithm for Optimal Placement of Renewable Distributed Generation and Storage units in Distribution Networks

Abstract

This paper presents a multi-objective algorithm to support sizing and placement of Renewable Distributed Generation with storage units (RDG&S) in radial distribution networks. Two objectives are considered in the model, the first one is focused in the minimization of the RDG&S units capital costs and the second one in the minimization of system losses. This approach uses a hybrid Ant Colony Genetic Algorithm (ACGA) divided in two steps. At the first step of the approach an Ant Colony (AC) acts to face with the uncertainty of the problem and to deal with instabilities of the initial data. This way a good Pareto front, which is used to feed the initial population of da Genetic Algorithm (GA). At the second step, an Elitist Robust Genetic Algorithm with a secondary population is used, to characterize the non-dominated Pareto Optimal Frontier. In this algorithm the concept of robustness is operationalized in the computation of the fitness value assigned to solutions. The results presented in this approach demonstrates the real capabilities of the proposed algorithm to generate a well-spread and more robust effective non-dominated Pareto Optimal Frontier.